Marine cone snail venoms consist of large, naturally occurring combinatorial libraries of disulfide-constrained peptide neurotoxins known as conotoxins, which have profound potential in the development of analgesics. In this study, we report a synthetic combinatorial strategy that probes the hypervariable regions of conotoxin frameworks to discover novel analgesic agents by utilizing high diversity mixture-based positional-scanning synthetic combinatorial libraries (PS-SCLs). We hypothesized that the direct in vivo testing of these mixture-based combinatorial library samples during the discovery phase would facilitate the identification of novel individual compounds with desirable antinociceptive profiles while simultaneously eliminating many compounds with poor activity or liabilities of locomotion and respiration. A PS-SCL was designed based on the α-conotoxin RgIA-ΔR n-loop region and consisted of 10,648 compounds systematically arranged into 66 mixture samples. Mixtures were directly screened in vivo using the mouse 55 °C warm-water tail-withdrawal assay, which allowed deconvolution of amino acid residues at each position that confer antinociceptive activity. A second generation library of 36 individual α-conotoxin analogues was synthesized using systematic combinations of amino acids identified from PS-SCL deconvolution and further screened for antinociceptive activity. Six individual analogues exhibited comparable antinociceptive activity to that of the recognized analgesic α-conotoxin RgIA-ΔR, and were selected for further examination of antinociceptive, respiratory, and locomotor effects. Three lead compounds were identified that produced dose-dependent antinociception without significant respiratory depression or decreased locomotor activity. Our results represent a unique approach for rapidly developing novel lead α-conotoxin analogues as low-liability analgesics with promising therapeutic potential.

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